Cross-country policy comparison of 30 km/h speed limits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
30 km/h speed zones are one of the most cost-effective road safety interventions to enhance the safety and liveability of local streets. However, only two zones are currently implemented in the state of Victoria, Australia, and these zones are not widely adopted across Australia. Greater understanding of the barriers to implementation is needed to rapidly advance implementation of this effective road safety intervention. We aimed to identify and explore barriers and enablers of implementation of 30 km/h speed limits in the state of Victoria, Australia, and compare this to implementation in an area where 30 km/h speed zones have been implemented successfully – British Columbia, Canada. We conducted 26 semi-structured interviews with relevant policy partners. Data were analysed abductively through reflexive thematic analysis. Six key themes were identified: (i) Appetite for change; (ii) Policy misalignment; (iii) Lack of local evidence; (iv) Council capacity; (v) Need for ‘self-explaining’ roads, and (vi) Equity lessons in implementation. We demonstrated momentum and support for 30 km/h speed zones. However, state government policy reform is needed to enable easier implementation by local councils. This study provides critical insights into the complexity of implementing road safety interventions and opportunities to enhance systems to catalyse change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it